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\title{An Agent that Presents Interesting and Relevant Historical Facts}
\author{
Georg~Albrecht \and
Peter~Mawhorter \and
Trevor~Pesout \and
Daniel~Sherman \and
Gregory~Weller 
}
\date{2010-3-9}

\begin{document}

\frame{\titlepage}

\begin{frame}
\frametitle{Goals}
\begin{enumerate}
\Large
\setlength{\itemsep}{2em}

\item Discuss a particular topic using a topic-specific fact database.

\item Automatically extract the facts from the Simple English Wikipedia.

\item Strategically present facts related to the current subject.
\end{enumerate}
\end{frame}

\begin{frame}
\frametitle{Architecture}
\begin{itemize}
\large
\setlength{\itemsep}{1em}

\item Extractor - Parses Simple English Wikipedia pages and extracts facts.

\item Understander - Parses user input to classify responses and recognize directions.

\item Presenter - Presents a selected fact or group of facts as natural language.

\item Decider - Searches for relevant facts to present.

\item Manager - Picks dialogue strategies and manages the other modules.

\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Extractor}

\begin{itemize}
\large
\setlength{\itemsep}{1em}

\item Procedure:
\begin{enumerate}
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\item Cleaning: removes HTML tags, resolves HTML entities, tokenizes into sentences of words.

\item Parsing: chunks named entities, parses structure based on tags.

\item Analysis: resolves pronouns, transforms known sentence structures into \texttt{Fact} objects.

\end{enumerate}

\item Successful parse rate is only 10-20\%.

\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Understander}
\begin{itemize}
\large
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\item Detect user responses to ``\texttt{Did you know...?}'' phrasings.

\item Extract user subject by chunking noun phrases.

\item Special thanks to \texttt{nltk.pos\_tag()} and \texttt{nltk.RegexpParser()}.

\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Presenter}
\begin{itemize}
\large
\setlength{\itemsep}{1em}

\item Template-based generation.

\item Conversational facts are different from ontological \textsc{Facts}:
\begin{itemize}
\item \textsc{Person} and \textsc{Event} are \textsc{Facts}.
\item ``\texttt{Leonardo da Vinci was born in 1452}'' is a fact.
\item Presented facts are relations between ontological \textsc{Facts}.
\end{itemize}

\item Considers desired behavior and relation to be presented.

\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Decider}
\begin{itemize}
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\item Chooses facts based on conversation state.

\item Selects specific \textsc{Fact} fields to present.

\item Finds related facts using location or date information.

\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Manager}
\begin{itemize}
\large
\setlength{\itemsep}{1em}

\item Chooses dialogue behaviors:
\begin{itemize}
\item \textsc{DeclareSingle}
\item \textsc{DeclareMultiple}
\item \textsc{Query}
\item \textsc{Transition}
\end{itemize}

\item Stores conversation state:
\begin{itemize}
\item Behavior history
\item Facts presented
\item User responses
\end{itemize}

\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Questions?}
\begin{itemize}
\color{Gray}
\large
\setlength{\itemsep}{1em}

\item Extractor - Parses Simple English Wikipedia pages and extracts facts.

\item Understander - Parses user input to classify responses and recognize directions.

\item Presenter - Presents a selected fact or group of facts as natural language.

\item Decider - Searches for relevant facts to present.

\item Manager - Picks dialogue strategies and manages the other modules.

\end{itemize}
\end{frame}

\end{document}
